rarhsmm: Regularized Autoregressive Hidden Semi Markov Model

Fit Gaussian hidden Markov (or semi-Markov) models with / without autoregressive coefficients and with / without regularization. The fitting algorithm for the hidden Markov model is illustrated by Rabiner (1989) <doi:10.1109/5.18626>. The shrinkage estimation on the covariance matrices is based on the graphical lasso method by Freedman (2007) <doi:10.1093/biostatistics/kxm045>. The shrinkage estimation on the autoregressive coefficients uses the elastic net shrinkage detailed in Zou (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.

Version: 1.0.2
Depends: R (≥ 3.0.0)
Imports: glmnet
Published: 2017-04-19
Author: Zekun (Jack) Xu, Ye Liu
Maintainer: Zekun Xu <zekunxu at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: rarhsmm results


Reference manual: rarhsmm.pdf
Package source: rarhsmm_1.0.2.tar.gz
Windows binaries: r-devel: rarhsmm_1.0.2.zip, r-release: rarhsmm_1.0.2.zip, r-oldrel: rarhsmm_1.0.2.zip
OS X El Capitan binaries: r-release: rarhsmm_1.0.2.tgz
OS X Mavericks binaries: r-oldrel: rarhsmm_1.0.2.tgz
Old sources: rarhsmm archive


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